Assimilation of meteorological and remote sensing data for snowmelt runoff forecasting

A versatile data assimilation scheme for remote sensing snow cover products and meteorological data was developed, aimed at operational use for short-term runoff forecasting. Spatial and temporal homogenisation of the various input data sets is carried out, including meteorological point measurements from stations, numerical weather predictions, and snow maps from satellites. The meteorological data are downscaled to match the scale of the snow products, derived from optical satellite images of MODIS and from radar images of Envisat ASAR. Snow maps from SAR and optical imagery reveal systematic differences which need to be compensated for use in snowmelt models. We applied a semi-distributed model to demonstrate the use of satellite snow cover data for short-term runoff forecasting. During the snowmelt periods 2005 and 2006 daily runoff forecasts were made for the drainage basin Ötztal (Austrian Alps) for time lags up to 6 days. Because satellite images were obtained intermittently, prognostic equations were applied to predict the daily snow cover extent for model update. Runoff forecasting uncertainty is estimated by using not only deterministic meteorological predictions as input, but also 51 ensemble predictions of the EPS system of the European Centre for Medium Range Weather Forecast. This is particularly important for water management tasks, because meteorological forecasts are the main error source for runoff prediction, as confirmed by simulation studies with modified input data from the various sources. Evaluation of the runoff forecasts reveals good agreement with the measurements, confirming the usefulness of the selected data processing and assimilation scheme for operational use.     

On the relation between HCMM satellite data and temperatures from standard meteorological sites in complex terrain

Data from the Heat Capacity Mapping Mission Experiment Satellite (HCMM) are used to plot lines of constant temperature at 1° intervals for the city of Melbourne and surrounding country. Using four individual scenes, the relationship between uncalibrated, i.e. relative, surface temperature and screen daily minimum air temperature at some 26 standard meteorological stations in the greater Melbourne region was studied. It was found that the relation between the two data sources is poor for the sites taken separately but that means of daily minimum temperatures for appropriately grouped meteorological sites show a consistent linear relationship with night-time HCMM data. The HCMM data also show significant variation in surface temperatures within short distances from meteorological sites and it is concluded that surface temperatures in such an area vary on a spatial scale that is large compared with the area sampled by a standard meteorological site but small compared with an HCMM pixel. The implications are that a number of sites arc needed to characterize a region independently of site-specific effects (i.e. that appropriately grouped sites can under some circumstances be used for calibrating satellite thermal data) and that thermal imagery could provide criteria for the selection of new standard meteorological sites.     

Schema Evolution in Data Warehouses

In this paper, we address the issues related to the evolution and maintenance of data warehousing systems, when underlying data sources change their schema capabilities. These changes can invalidate views at the data warehousing system. We present an approach for dynamically adapting views according to schema changes arising on source relations. This type of maintenance concerns both the schema and the data of the data warehouse. The main issue is to avoid the view recomputation from scratch especially when views are defined from multiple sources. The data of the data warehouse is used primarily in organizational decision-making and may be strategic. Therefore, the schema of the data warehouse can evolve for modeling new requirements resulting from analysis or data-mining processing. Our approach provides means to support schema evolution of the data warehouse independently of the data sources. Received 20 March 2000 / Revised 5 January 2001 / Accepted in revised form 20 April 2001     

基于Landsat影像的ALEXI模型小流域验证

基于静止卫星数据开发的陆气相互作用模型(ALEXI模型)为地表能量平衡过程分析提供了大尺度空间拓展,为认识大尺度的陆气相互作用提供了新途径,已被应用于干旱监测、流域水文分析以及气候变化研究。使用高精度卫星分辨率获得的通量结果对ALEXI进行初步的验证与评估,选择下垫面类型复杂的小流域为研究区,以比较成熟的基于Landsat流域分析的SEBAL模型结果为验证源,对比分析同时期ALEXI模型的地表通量结果,研究发现ALEXI模型与SEBAL模型能够反映较一致的地表能量交换信息格局,统计分析能够得到较一致的结果。此外,由于模型自身的限制因素以及地表观测误差的影响,还需进一步开展定量对比验证方面的工作。     

A continuous measure of gross primary production for the conterminous United States derived from MODIS and AmeriFlux data

The quantification of carbon fluxes between the terrestrial biosphere and the atmosphere is of scientific importance and also relevant to climate-policy making. Eddy covariance flux towers provide continuous measurements of ecosystem-level exchange of carbon dioxide spanning diurnal, synoptic, seasonal, and interannual time scales. However, these measurements only represent the fluxes at the scale of the tower footprint. Here we used remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to upscale gross primary productivity (GPP) data from eddy covariance flux towers to the continental scale. We first combined GPP and MODIS data for 42 AmeriFlux towers encompassing a wide range of ecosystem and climate types to develop a predictive GPP model using a regression tree approach. The predictive model was trained using observed GPP over the period 2000-2004, and was validated using observed GPP over the period 2005-2006 and leave-one-out cross-validation. Our model predicted GPP fairly well at the site level. We then used the model to estimate GPP for each 1 km × 1 km cell across the U.S. for each 8-day interval over the period from February 2000 to December 2006 using MODIS data. Our GPP estimates provide a spatially and temporally continuous measure of gross primary production for the U.S. that is a highly constrained by eddy covariance flux data. Our study demonstrated that our empirical approach is effective for upscaling eddy flux GPP data to the continental scale and producing continuous GPP estimates across multiple biomes. With these estimates, we then examined the patterns, magnitude, and interannual variability of GPP. We estimated a gross carbon uptake between 6.91 and 7.33 Pg C yr^− 1 for the conterminous U.S. Drought, fires, and hurricanes reduced annual GPP at regional scales and could have a significant impact on the U.S. net ecosystem carbon exchange. The sources of the interannual variability of U.S. GPP were dominated by these extreme climate events and disturbances.     

Modified light use efficiency model for assessment of carbon sequestration in grasslands of Kazakhstan: combining ground biomass data and remote-sensing

A modified light use efficiency (LUE) model was tested in the grasslands of central Kazakhstan in terms of its ability to characterize spatial patterns and interannual dynamics of net primary production (NPP) at a regional scale. In this model, the LUE of the grassland biome (?_n) was simulated from ground-based NPP measurements, absorbed photosynthetically active radiation (APAR) and meteorological observations using a new empirical approach. Using coarse-resolution satellite data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), monthly NPP was calculated from 1998 to 2008 over a large grassland region in Kazakhstan. The modelling results were verified against scaled up plot-level observations of grassland biomass and another available NPP data set derived from a field study in a similar grassland biome. The results indicated the reliability of productivity estimates produced by the model for regional monitoring of grassland NPP. The method for simulation of ?_n suggested in this study can be used in grassland regions where no carbon flux measurements are accessible.     

Multi-year simulation of Mediterranean forest transpiration by the integration of NOAA-AVHRR and ancillary data

The estimation of transpiration fluxes through wide vegetated land surfaces is of great importance for the proper planning and management of environmental resources, particularly in areas where water is a main limiting factor during at least part of the growing cycle. While remotely sensed techniques cannot directly measure these fluxes, they can provide useful information on vegetation variables such as Leaf Area Index (LAI), which are functionally related to the mentioned processes. The aims of the present work were: (a) to illustrate the use of multi-temporal LAI profiles derived from National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) Normalized Difference Vegetation Index (NDVI) data as input for a biogeochemical model (Forest-BGC) which simulates the main processes of forest vegetation (transpiration and photosynthesis); and (b) to analyse the sensitivity of the calibrated model to its main driving variables (meteorological data and NDVI-derived LAI profiles) in order to assess their relative importance for operational transpiration monitoring. In particular, the model was applied to two oak stands in the Tuscany Region (central Italy), which are representative of Mediterranean forests and for which a calibration phase had already been performed. Simulations were carried out for a 15-year period (1986–2000) using as inputs daily meteorological data and NDVI-derived monthly LAI profiles. The sensitivity of the model to both input types was then assessed through other model runs with fixed values of the two variables. The results of these experiments indicated that the remotely sensed LAI estimates are the main determinant of simulated transpirations, especially during the Mediterranean arid season (summer) when water resources are the primary limiting factor for vegetation development.     

On the relationship of NDVI with leaf area index in a deciduous forest site

Numerous studies have reported on the relationship between the normalized difference vegetation index (NDVI) and leaf area index (LAI), but the seasonal and annual variability of this relationship has been less explored. This paper reports a study of the NDVI-LAI relationship through the years from 1996 to 2001 at a deciduous forest site. Six years of LAI patterns from the forest were estimated using a radiative transfer model with input of above and below canopy measurements of global radiation, while NDVI data sets were retrieved from composite NDVI time series of various remote sensing sources, namely NOAA Advanced Very High Resolution Radiometer (AVHRR; 1996, 1997, 1998 and 2000), SPOT VEGETATION (1998-2001), and Terra MODIS (2001). Composite NDVI was first used to remove the residual noise based on an adjusted Fourier transform and to obtain the NDVI time-series for each day during each year.The results suggest that the NDVI-LAI relationship can vary both seasonally and inter-annually in tune with the variations in phenological development of the trees and in response to temporal variations of environmental conditions. Strong linear relationships are obtained during the leaf production and leaf senescence periods for all years, but the relationship is poor during periods of maximum LAI, apparently due to the saturation of NDVI at high values of LAI. The NDVI-LAI relationship was found to be poor (R² varied from 0.39 to 0.46 for different sources of NDVI) when all the data were pooled across the years, apparently due to different leaf area development patterns in the different years. The relationship is also affected by background NDVI, but this could be minimized by applying relative NDVI.Comparisons between AVHRR and VEGETATION NDVI revealed that these two had good linear relationships (R²=0.74 for 1998 and 0.63 for 2000). However, VEGETATION NDVI data series had some unreasonably high values during beginning and end of each year period, which must be discarded before adjusted Fourier transform processing. MODIS NDVI had values greater than 0.62 through the entire year in 2001, however, MODIS NDVI still showed an “M-shaped” pattern as observed for VEGETATION NDVI in 2001. MODIS enhanced vegetation index (EVI) was the only index that exhibited a poor linear relationship with LAI during the leaf senescence period in year 2001. The results suggest that a relationship established between the LAI and NDVI in a particular year may not be applicable in other years, so attention must be paid to the temporal scale when applying NDVI-LAI relationships.     

Performance analysis of trigger frame in enhanced UL and DL MU MIMO transmissions

In an Internet-of-Things (IoT) environment, congestion and scarcity problems may occur because many mobile stations (STAs) access wireless networks simultaneously. The IEEE 802.11ax/802.11be standards for large-scale wireless communications have defined a trigger frame (TF) to control multiple STAs. During resource allocation, the downlink (DL) transmission is divided in a control period from the access point (AP) to multiple STAs. The resource allocation (RA) is then assigned to an uplink (UL) transmission by a TF and a DL period from the AP to STAs. However, because the DL transmission should be considered separately in terms of the control and DL periods, it is necessary to analyze the DL transmission. We propose a scheduled MU transmission (SMT) algorithm for enhanced UL and DL MU MIMO transmissions. In this study, we analyze and systematically model medium access control (MAC) performance when the DL transmission is divided in the control and data periods when the UL coexists with the DL data transmission. To achieve this, we mathematically analyze the time-efficient throughput, estimate the transmission and collision probabilities for wireless local area network (WLAN) STAs, and generalize the transmission interval. In addition, we propose an access category (AC) for the TF that is defined in the DL transmission. All data transmissions are defined as the ACs for basic channel access, but the AC is not defined for the TF. Therefore, we clarify the transmission by defining the AC of the TF to control the UL transmissions of various STAs. Evaluation results demonstrate that the SMT algorithm can improve the MAC throughput by up to 70% – 87% compared to UL and DL MU MIMO transmissions.

     

A stochastic model for heterogeneous computing and its applicationin data relocation scheme development

In a dedicated, mixed-machine, heterogeneous computing (HC) system, an application program may be decomposed into subtasks, then each subtask assigned to the machine where it is best suited for execution. Data relocation is defined as selecting the sources for needed data items. It is assumed that multiple independent subtasks of an application program can be executed concurrently on different machines whenever possible. A theoretical stochastic model for HC Is proposed, in which the computation times of subtasks and communication times for intermachine data transfers can be random variables. The optimization problem for finding the optimal matching, scheduling, and data relocation schemes to minimize the total execution time of an application program is defined based on this stochastic HC model. The global optimization criterion and search space for the above optimization problem are described. It is validated that a greedy algorithm-based approach can establish a local optimization criterion for developing data relocation heuristics. The validation is provided by a theoretical proof based on a set of common assumptions about the underlying HC system and application program. The local optimization criterion established by the greedy approach, coupled with the search space defined for choosing valid data relocation schemes, can help developers of future practical data relocation heuristics     

Comprehensive drought index as an indicator for use in drought monitoring integrating multi-source remote sensing data: a case study covering the Sichuan-Chongqing region

Numerous meteorological drought-monitoring indices and remote-sensing-based spatial drought monitoring indices have been developed and applied to monitor drought in different ways. However, individual indices have obvious deficiencies in terms of their responses to drought, and they do not comprehensively reflect the available information on drought. To overcome issues with the data themselves and improve drought monitoring techniques, we use a comprehensive drought index (CDI) derived from the vegetation condition index, the temperature condition index, and the precipitation condition index to monitor meteorological or agricultural drought for the Sichuan-Chongqing region. To assess CDI performance, monthly CDI values for Sichuan-Chongqing region were used to analyse the spatial and temporal variations of the 2006 drought. The results indicated that all aspects of the drought were monitored, and the results were in agreement with related research. Meanwhile, an extreme drought was accurately explored using the CDI in the Sichuan-Chongqing region from 2000 to 2011. Finally, a validation was performed, and the results show that the CDI is closely related with the standardized precipitation index calculated using a 3-month time scale (SPI3), as well as variations in crop yield and drought-affected crop area. These results provide further evidence that the CDI is an indicator that can be used in integrated drought monitoring and that it can simultaneously reflect meteorological and agricultural drought information.     

A semi-empirical model for predicting hourly ground-level fine particulate matter (PM2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements

A semi-empirical model is developed to predict the hourly concentration of ground-level fine particulate matter (PM_2.5) coincident to satellite overpass, at a regional scale. The model corrects the aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) by the assimilated parameters characterizing the boundary layer and further adjusts the corrected value according to meteorological conditions near the ground. The model was built and validated using the data collected for southern Ontario, Canada for 2004. Overall, the model is able to explain 65% of the variability in ground-level PM_2.5 concentration. The model-predicted values of PM_2.5 mass concentration are highly correlated with the actual observations. The root-mean-square error of the model is 6.1 µg/m³. The incorporation of ground-level temperature and relative humidity is found to be significant in improving the model predictability. The coarse resolution of the assimilated meteorological fields limits their value in the AOD correction. Although MODIS AOD data is acquired on a daily basis and the valid data coverage can sometimes be very limited due to unfavourable weather conditions, the model provides a cost-effective approach for obtaining supplemental PM_2.5 concentration information in addition to the ground-based monitoring station measurement.     

基于PSO-GRU神经网络的青椒生长期需水预测

青椒生长期内需水量与气温、气压、相对湿度等因子之间存在复杂的非线性关系,需水量变化呈现出时序性和周期性的规律,为提高青椒生长期日均需水量的预测精度,提出一种PSO-GRU青椒生长期日均需水预测模型。以2014—2018年实验所得的青椒需水和气象环境等数据为数据源,将日均气温、气压、风速等六维数据作为特征集,需水量作为标签,采用GRU神经网络作为需水预测的训练模型,并针对GRU超参数容易陷入局部最优的问题,利用粒子群算法(PSO）优化GRU模型的超参数,通过仿真实验对青椒生长期日均需水量进行预测,并与RNN,LSTM和GRU等模型进行对比,验证PSO-GRU模型的优越性。仿真实验结果表明：PSO-GRU模型的预测精度和拟合效果显著提高,RMSE为0.505,MAE为0.388,MAPE为7.73,R2为0.888。PSO-GRU模型可为制定灌溉计划提供依据,有利于节水灌溉,推动农业种植水利信息化。     

twodee-2: A shallow layer model for dense gas dispersion on complex topography

twodee-2 is a Fortran 90 code based on previous code (twodee). It is designed to solve the shallow water equations for fluid depth, depth-averaged horizontal velocities and depth-averaged fluid density. The shallow layer approach used by twodee-2 is a compromise between the complexity of CFD models and the simpler integral models. It can be used for forecasting gas dispersion near the ground and/or for hazard assessment over complex terrains. The inputs to the model are topography, terrain roughness, wind measurements from meteorological stations and gas flow rate from the ground sources. Optionally the model can be coupled with the output of a meteorological processor which generates a zero-divergence wind field incorporating terrain effects. Model outputs are gas concentration, depth-averaged velocity, averaged cloud thickness and dose. The model can be a useful tool for gas hazard assessment by evaluating where and when lethal concentrations for humans and animals can be reached.     

Response of model simulated weather parameters to round-off-errors on different systems

In this study, the weather forecasting model of the National Centre for Medium Range Weather Forecasting (NCMRWF) is used for examining the characteristics of round-off-errors on three different computer architectures – PARAM 10K, SUNFIRE 6800 and Dec Alpha for several meteorological parameters such as precipitation, temperature at the surface and mid-atmosphere, and upper and lower level winds. It is well known that the implementation of floating point arithmetic varies from one computing system to another. As a result, meteorological parameters simulated by numerical models on two different systems may deviate from each other and the difference field becomes larger as the model is integrated for longer time, for example, in the scale of several months. This paper focuses on the reduction of such round-off-errors by a simple method of modifying the format representation of the initial data supplied to the model. In all the three systems, the model has been integrated for 4 months starting from 4^th May, 1996. It is found that after 5 days of model integration with the modified data, the round-off-errors become insignificant. The rate of reduction of round-off-errors is fast up to a month of model integration and thereafter the rate slows down and stabilises. It is further noticed that at the end of four months of integration, the reduction in round-off-errors over the tropical region and oceans is much more than over the rest of the globe.     

A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS

Many current models of ecosystem carbon exchange based on remote sensing, such as the MODIS product termed MOD17, still require considerable input from ground based meteorological measurements and look up tables based on vegetation type. Since these data are often not available at the same spatial scale as the remote sensing imagery, they can introduce substantial errors into the carbon exchange estimates. Here we present further development of a gross primary production (GPP) model based entirely on remote sensing data. In contrast to an earlier model based only on the enhanced vegetation index (EVI), this model, termed the Temperature and Greenness (TG) model, also includes the land surface temperature (LST) product from MODIS. In addition to its obvious relationship to vegetation temperature, LST was correlated with vapor pressure deficit and photosynthetically active radiation. Combination of EVI and LST in the model substantially improved the correlation between predicted and measured GPP at 11 eddy correlation flux towers in a wide range of vegetation types across North America. In many cases, the TG model provided substantially better predictions of GPP than did the MODIS GPP product. However, both models resulted in poor predictions for sparse shrub habitats where solar angle effects on remote sensing indices were large. Although it may be possible to improve the MODIS GPP product through improved parameterization, our results suggest that simpler models based entirely on remote sensing can provide equally good predictions of GPP.     

The GBA2000 initiative: Developing a global burnt area database from SPOT-VEGETATION imagery

The scientific community dealing with modelling of emissions of greenhouse gases and aerosols from anthropogenic sources demands reliable and quantitative information on the magnitude of biomass burning at a global scale. It is in this context that the Global Burnt Area -- 2000 (GBA2000) initiative has been launched. The specific objectives of this initiative are to produce a map of the areas burnt globally for the year 2000, using the medium resolution (1.1 km) Système Pour l'Observation de la Terre (SPOT) 4-VEGETATION (SPOT-VGT) satellite imagery and to derive statistics of area burnt per country, per month and per main type of vegetation cover. A series of regional algorithms has been developed and incorporated into a data processing system designed to yield monthly estimates of areas burnt at a global scale. The map data will then be transformed into quantitative information and made publicly available over the World Wide Web at a range of spatial and temporal resolutions to satisfy some of the requirements of the atmospheric and climate change modelling community.     

基于HBase的气象地面分钟数据分布式存储系统

针对气象地面分钟数据要素多样、信息量大、产生频次高等特点,传统的关系型数据库系统在存储和管理数据上出现负载饱满、读写性能不理想等问题。结合对分布式数据库HBase的存储模型的研究,行主键(row key)采用时间加站号的方式设计了气象分钟数据存储结构模型,实现对海量气象数据的分布式存储和元信息管理。对HBase的唯一索引在面对气象业务的复杂查询用例时响应时间过长的问题,使用搜索引擎solr提供的API接口并参考气象业务中的查询用例对相关字段建立辅助索引,来满足业务检索时效。实验结果表明,该系统具有很好的存储能力和检索效率,入库效率最高可达每秒34000条,并且在常规查询用例的结果返回时效达到毫秒级,能够满足大规模气象数据在业务应用中对存储和查询时效的性能要求。     

A daily weather generator for use in climate change studies

This paper describes the development of a weather generator for use in climate impact assessments of agricultural and water system management. The generator produces internally consistent series of meteorological variables including: rainfall, temperature, humidity, wind, sunshine, as well as derivation of potential evapotranspiration. The system produces series at a daily time resolution, using two stochastic models in series: first, for rainfall which produces an output series which is then used for a second model generating the other variables dependent on rainfall. The series are intended for single sites defined nationally across the UK at a 5 km resolution, but can be generated to be representative across small catchments (<1000 km²). Scenarios can be generated for the control period (1961–1990) based on observed data, as well as for the UK Climate Impacts Programme (UKCIP02) scenarios for three time slices (2020s, 2050s and 2080s). Future scenarios are generated by fitting the models to observations which have been perturbed by application of change factors derived from the UKCIP02 mean projected changes in that variable. These change factors are readily updated, as new scenarios become available, and with suitable calibration data the approach could be extended to any geographical region.